86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
# -*- coding:utf-8 -*-
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import cv2
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import numpy as np
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import pyclipper
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from shapely.geometry import Polygon
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__all__ = ['MakePseGt']
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class MakePseGt(object):
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r'''
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Making binary mask from detection data with ICDAR format.
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Typically following the process of class `MakeICDARData`.
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'''
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def __init__(self, kernel_num=7, size=640, min_shrink_ratio=0.4, **kwargs):
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self.kernel_num = kernel_num
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self.min_shrink_ratio = min_shrink_ratio
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self.size = size
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def __call__(self, data):
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image = data['image']
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text_polys = data['polys']
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ignore_tags = data['ignore_tags']
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h, w, _ = image.shape
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short_edge = min(h, w)
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if short_edge < self.size:
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# keep short_size >= self.size
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scale = self.size / short_edge
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image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
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text_polys *= scale
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gt_kernels = []
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for i in range(1,self.kernel_num+1):
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# s1->sn, from big to small
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rate = 1.0 - (1.0 - self.min_shrink_ratio) / (self.kernel_num - 1) * i
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text_kernel, ignore_tags = self.generate_kernel(image.shape[0:2], rate, text_polys, ignore_tags)
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gt_kernels.append(text_kernel)
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training_mask = np.ones(image.shape[0:2], dtype='uint8')
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for i in range(text_polys.shape[0]):
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if ignore_tags[i]:
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cv2.fillPoly(training_mask, text_polys[i].astype(np.int32)[np.newaxis, :, :], 0)
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gt_kernels = np.array(gt_kernels)
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gt_kernels[gt_kernels > 0] = 1
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data['image'] = image
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data['polys'] = text_polys
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data['gt_kernels'] = gt_kernels[0:]
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data['gt_text'] = gt_kernels[0]
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data['mask'] = training_mask.astype('float32')
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return data
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def generate_kernel(self, img_size, shrink_ratio, text_polys, ignore_tags=None):
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h, w = img_size
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text_kernel = np.zeros((h, w), dtype=np.float32)
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for i, poly in enumerate(text_polys):
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polygon = Polygon(poly)
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distance = polygon.area * (1 - shrink_ratio * shrink_ratio) / (polygon.length + 1e-6)
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subject = [tuple(l) for l in poly]
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pco = pyclipper.PyclipperOffset()
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pco.AddPath(subject, pyclipper.JT_ROUND,
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pyclipper.ET_CLOSEDPOLYGON)
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shrinked = np.array(pco.Execute(-distance))
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if len(shrinked) == 0 or shrinked.size == 0:
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if ignore_tags is not None:
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ignore_tags[i] = True
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continue
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try:
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shrinked = np.array(shrinked[0]).reshape(-1, 2)
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except:
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if ignore_tags is not None:
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ignore_tags[i] = True
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continue
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cv2.fillPoly(text_kernel, [shrinked.astype(np.int32)], i + 1)
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return text_kernel, ignore_tags
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